Visual feature learning in artificial grammar classification
Visual feature learning in artificial grammar classification
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DOI：
10.1037/0278-7393.30.3.714
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发表时间：
2004-05-01
影响因子：
2.6
通讯作者：
Knowlton, BJ
中科院分区：
文献类型：
--
作者：
Chang, GY;Knowlton, BJ
人工语法学习任务已被广泛用于评估个人的内隐学习能力。以往的工作表明，参与者隐式地获得基于规则的知识，以及在这个任务中的范例特定的知识。本研究考察了样例特定知识的获得是否基于样例的视觉特征。当研究和测试之间的字体和情况发生变化时，分类判断中对语法规则的敏感性没有影响。然而，这样的变化实际上消除了分类判断中对字母二元组和三元组（组块强度）的训练频率的敏感性。在研究过程中的次要任务的性能消除了这种字体的敏感性，并普遍减少了块强度知识的贡献。实验结果与知觉流畅性对人工语法判断的贡献一致。
The Artificial Grammar Learning task has been used extensively to assess individuals' implicit learning capabilities. Previous work suggests that participants implicitly acquire rule-based knowledge as well as exemplar-specific knowledge in this task. This study investigated whether exemplar-specific knowledge acquired in this task is based on the visual features of the exemplars. When a change in the font and case occurred between study and test, there was no effect on sensitivity to grammatical rules in classification judgments. However, such a change did virtually eliminate sensitivity to training frequencies of letter bigrams and trigrams (chunk strength) in classification judgments. Performance of a secondary task during study eliminated this font sensitivity and generally reduced the contribution of chunk strength knowledge. The results are consistent with the idea that perceptual fluency makes a contribution to artificial grammar judgments.